Model Registry: Governance for Deployable Artifacts
The model registry as the source of truth between ML and production: versioned model records with stages/aliases, lineage to training runs and data versions, approval workflows, artifact integrity, and how serving consumes it for canaries, rollback, and audit.
01.Why Pickles Are Not a Deployment Strategy
A team's models become a product when anyone can answer: which version is live, who approved it, what data trained it, and how do we roll back in 90 seconds? Without a registry the answers are tribal. The registry is the ML-world equivalent of a container registry plus a change log:
- Model records: name ("homepage-ranker") -> ordered versions (v13, v14), each pointing at artifacts (ONNX/checkpoint/TensorRT engine variants) in object storage with checksums.
- Stages/aliases: MLflow 2.x's
Production/Staging/Archivedmigrated to aliases (@champion,@challenger,@canary); SageMaker Model Registry has approval statuses; Vertex has versions with labels. Same semantics: deployment intent as metadata. - Lineage: version -> training run URI (tracker) -> git SHA + data version (DVC rev / table snapshot). This is what makes "retrain from evidence" possible.
- Governance: approval transitions require a human/system identity; regulated industries add sign-off fields (EU AI Act documentation expectations made this concrete in 2024-2026 audits).
Promotion Ladder in a Model Registry
Promotion Ladder in a Model Registry
Models climb stages/aliases (pending -> challenger -> champion) only by passing machine-checked gates and recorded approvals; serving resolves aliases, so promotion and rollback are flag flips against the registry rather than redeploys.
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